1. Field of the Invention
The present invention relates to the field of fabricating semiconductor devices, and, in particular, to advanced process control (APC) techniques for manufacturing processes, wherein an improved process control quality is achieved by adjusting process parameters on the basis of a process model, measurement data and information related to, for example, the product, the type of process, the process tool to be used and the like.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of semiconductor fabrication, since, here, it is essential to combine cutting edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve process tool utilization. The latter aspect is especially important, since, in modern semiconductor facilities, equipment is required which is extremely cost-intensive and represents the dominant part of the total product costs. For example, in manufacturing modern integrated circuits, 500 or more individual processes may be necessary to complete the integrated circuit, wherein failure in a single process step may result in a loss of the complete integrated circuit. This problem is even exacerbated in that the size of the substrate on which a plurality of such integrated circuits are processed steadily increases, so that failure in a single process step may entail the loss of a large number of products.
Therefore, the various manufacturing stages have to be thoroughly monitored to avoid undue waste of tool operation time and raw materials. Ideally, the effect of each individual process step on each substrate would be detected by measurement and the substrate under consideration would be released for further processing only if the required specifications were met. A corresponding process control, however, is not practical, since measuring the effects of certain processes may require relatively long measurement times or may even necessitate the destruction of the sample. Moreover, immense effort, in terms of time and equipment, would have to be made on the metrology side to provide the required measurement results. Additionally, utilization of the process tool would be minimized since the tool would be released only after the provision of the measurement result and its assessment.
The introduction of statistical methods, e.g., mean values, control charts, etc., for identifying out-of-control situations and adjusting process parameters significantly relaxes the above problem and allows a moderate utilization of the process tools while attaining a relatively high product yield. Nevertheless, in total, a large number of dummy substrates or pilot substrates may be necessary to adjust process parameters of respective process tools, wherein tolerable parameter drifts during the process have to be taken into consideration when designing a process sequence, since such parameter drifts may remain undetected over a long time period or may not be efficiently compensated for.
Recently, a process control strategy has been introduced, and is continuously being improved, allowing a high degree of process control, desirably on a run-to-run basis, with a moderate amount of measurement data. In this control strategy, so-called advanced process control, a model of a process or of a group of interrelated processes is established and implemented in an appropriately configured process controller. The process controller also receives information including pre-process measurement data and/or post-process measurement data as well as information related, for instance, to the substrate history, such as type of process or processes, the product type, the process tool or process tools in which the products are to be processed or have been processed in previous steps, the process recipe to be used, i.e., a set of required sub-steps for the process or processes under consideration, wherein possibly fixed process parameters and variable process parameters may be contained, and the like. From this information and the process model, the process controller determines a controller state or process state that describes the effect of the process or processes under consideration on the specific product, thereby permitting the establishment of an appropriate parameter setting of the variable parameters of the specified process recipe to be performed with the substrate under consideration.
In complex APC algorithms, the measurement data contained in the information supplied to the APC algorithm, which may represent feedback and/or feedforward information, is typically organized in the form of a segregated structure. That is, based on the measurement data and any additional information, such as information on an upstream operation, the type of product, previously used process tools and the like, which may have an influence on the result of the process to be controlled, and on the basis of a respective process model, the APC algorithm determines the parameter setting to be used in the current process. Due to the various influences, such as the upstream operation, the product type and the like, the information supplied to the APC algorithm is “clustered” or segregated with the intention to use that information supplied to the APC algorithm such that it fits best the current process situation or condition of the process tool to be controlled.
With reference to FIGS. 1a-1b, an exemplary APC architecture may be described in more detail, wherein appropriately clustered or segregated data are used for defining respective control situations or manufacturing contexts for the APC algorithm.
In FIG. 1a, a typical portion of a manufacturing environment 100 is schematically illustrated, wherein the environment 100 may be configured to form resist features on semiconductor devices in a controlled fashion. For instance, the environment 100 may represent a manufacturing sequence required for establishing resist features for the formation of gate electrodes of transistor devices in advanced semiconductor devices. The environment 100 comprises a first photolithography tool S1, which is also referred to as a stepper, and a second photolithography tool S2, which are to be controlled by an APC algorithm implemented in a correspondingly configured controller 110. Moreover, a first and a second process tool for applying a photoresist on a substrate, referred to as P1 and P2, are provided and may represent upstream process tools, the operation of which may influence the performance of the steppers S1 and S2. Moreover, two different types of substrates, indicated as type A and type B, may be introduced into the environment 100 as a group of substrates, as individual substrates, and the like. Moreover, a metrology tool 120, for instance an optical instrument for estimating a line width of resist features, is provided and is operatively connected to the controller 110 so as to provide measurement results. Thus, the controller 110 and the metrology tool 120 establish a feedback control loop, in which current tool parameter settings for the steppers S1 and S2 are calculated on the basis of previously processed substrates. The controller 110 is further configured to receive additional information regarding the type of substrate, the process tools used, and reticles R1, R2 which may be used in the steppers S1 and S2.
During a typical manufacturing sequence in the environment 100, substrates A and B are processed in the resist coating tools P1 and P2 in conformity with process requirements as dictated by tool availability and the like. Thereafter, the substrates arrive at the steppers S1 and S2, the parameter settings of which are determined by respective process recipes, wherein the specific settings of any variable parameters, such as exposure dose and the like, are provided by the controller 110, which calculates, for instance, an appropriate exposure dose on the basis of the measurement results of previously processed substrates and the tool-specific information. When configuring the controller 110, a structure of the information supplied thereto may be established so as to estimate the state of a process to be performed in the tools S1 and S2 on the basis of information that is segregated according to this structure. For instance, for the APC algorithm of the controller 110, four items of relevance may have been identified, for which the following structure may have been established: first—upstream entity, that is, the resist coating tools P1 and P2; second—current entity, that is, the steppers S1 and S2; third—product type, that is substrates of the type A and B; and fourth—reticles R1 and R2 used in the steppers S1 and S2.
FIG. 1b schematically shows in a more convenient fashion a respective structure 130, wherein each item of the last row defines a respective manufacturing context. Hence, when the APC controller 110 is operating on the basis of this structure, there are sixteen different “types” of controller data, wherein each type is individually used for calculating the respective parameter setting for the steppers S1 or S2 for that specific manufacturing context that is associated with this individual set of controller data. In other words, the controller 110 may treat the measurement data obtained from the tool 120 differently for the sixteen end points of the structure shown in FIG. 1b so as to take into consideration the various process situations represented by the end points of the tree structure 130. Thus, during the processing of a plurality of substrates A and B in the environment 100, an increasing amount of metrology data is created, which is grouped or clustered into respective manufacturing contexts. That is, new substrates of the types A and B to be processed will receive the process settings, which are calculated by using only data from previously processed substrates that had the same values for all the items in the structure 130. When, for instance, one of the contexts, such as the context specified by the quadruple (P1, S2, B, R2) occurs for the first time or the last occurrence of this context is considered as being too long ago, the state of the context may not reliably be estimated due to missing data or aged data. Therefore, this context is typically initialized by running pilot substrates in order to obtain sufficient data to estimate the “actual” control state and to perform a control operation on the basis of data obtained by the pilot substrates. However, the processing of pilot substrates may be extremely costly and time consuming, thereby reducing throughput, tool utilization and, finally, profitability.
In view of the situation described above, there is a need for an enhanced technique in adapting APC algorithms while avoiding or at least reducing the effects of one or more of the problems identified above.